


import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase		   =			logging.get_logger(__name__)

lowerCamelCase		   =			{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}




class A    (  _A	):
      UpperCamelCase__				:    Dict ='mvp'
      UpperCamelCase__				:    Dict =['past_key_values']
      UpperCamelCase__				:    int ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






      def __init__(   self		:	Any     ,  lowercase_		:	Dict=5_0267     ,  lowercase_		:	Optional[Any]=1024     ,  lowercase_		:	List[Any]=12     ,  lowercase_		:	Tuple=4096     ,  lowercase_		:	Optional[int]=16     ,  lowercase_		:	Dict=12     ,  lowercase_		:	Optional[int]=4096     ,  lowercase_		:	Tuple=16     ,  lowercase_		:	Any=0.0     ,  lowercase_		:	Optional[Any]=0.0     ,  lowercase_		:	Optional[int]="gelu"     ,  lowercase_		:	int=1024     ,  lowercase_		:	List[Any]=0.1     ,  lowercase_		:	Any=0.0     ,  lowercase_		:	Optional[Any]=0.0     ,  lowercase_		:	Any=0.02     ,  lowercase_		:	List[str]=0.0     ,  lowercase_		:	Any=False     ,  lowercase_		:	str=True     ,  lowercase_		:	Optional[Any]=1     ,  lowercase_		:	List[Any]=0     ,  lowercase_		:	Union[str, Any]=2     ,  lowercase_		:	Any=True     ,  lowercase_		:	Tuple=2     ,  lowercase_		:	Optional[int]=2     ,  lowercase_		:	str=False     ,  lowercase_		:	Union[str, Any]=100     ,  lowercase_		:	Optional[int]=800     ,  **lowercase_		:	Optional[Any]     ,  )   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =vocab_size
             _lowerCamelCase			:						Dict            =max_position_embeddings
             _lowerCamelCase			:						Any            =d_model
             _lowerCamelCase			:						List[str]            =encoder_ffn_dim
             _lowerCamelCase			:						List[Any]            =encoder_layers
             _lowerCamelCase			:						Union[str, Any]            =encoder_attention_heads
             _lowerCamelCase			:						List[Any]            =decoder_ffn_dim
             _lowerCamelCase			:						Tuple            =decoder_layers
             _lowerCamelCase			:						Optional[int]            =decoder_attention_heads
             _lowerCamelCase			:						str            =dropout
             _lowerCamelCase			:						Tuple            =attention_dropout
             _lowerCamelCase			:						Any            =activation_dropout
             _lowerCamelCase			:						str            =activation_function
             _lowerCamelCase			:						List[Any]            =init_std
             _lowerCamelCase			:						Union[str, Any]            =encoder_layerdrop
             _lowerCamelCase			:						Union[str, Any]            =decoder_layerdrop
             _lowerCamelCase			:						Optional[Any]            =classifier_dropout
             _lowerCamelCase			:						Union[str, Any]            =use_cache
             _lowerCamelCase			:						Optional[Any]            =encoder_layers
             _lowerCamelCase			:						int            =scale_embedding  # scale factor will be sqrt(d_model) if True
             _lowerCamelCase			:						int            =use_prompt
             _lowerCamelCase			:						Optional[int]            =prompt_length
             _lowerCamelCase			:						Optional[int]            =prompt_mid_dim

             super().__init__(
                 pad_token_id=UpperCamelCase__     ,  bos_token_id=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     ,  is_encoder_decoder=UpperCamelCase__     ,  decoder_start_token_id=UpperCamelCase__     ,  forced_eos_token_id=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

             if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'     ,  UpperCamelCase__					):
                    _lowerCamelCase			:						Any            =self.bos_token_id
                    warnings.warn(
                        F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                        'The config can simply be saved and uploaded again to be fixed.'					)




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						["""image_processor""", """tokenizer"""]
  a__       			=						"""BridgeTowerImageProcessor"""
  a__       			=						("""RobertaTokenizer""", """RobertaTokenizerFast""")
  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    super().__init__(UpperCamelCase__     ,				UpperCamelCase__       )
  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Union[bool, str, PaddingStrategy] = False     ,				UpperCamelCase__     :					Union[bool, str, TruncationStrategy] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				**UpperCamelCase__     :					Dict     ,				)     ->      BatchEncoding:







    """simple docstring"""






    __magic_name__								=			self.tokenizer(
        text=UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				stride=UpperCamelCase__     ,				pad_to_multiple_of=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				return_overflowing_tokens=UpperCamelCase__     ,				return_special_tokens_mask=UpperCamelCase__     ,				return_offsets_mapping=UpperCamelCase__     ,				return_length=UpperCamelCase__     ,				verbose=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    # add pixel_values + pixel_mask
    __magic_name__								=			self.image_processor(
        UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				do_normalize=UpperCamelCase__     ,				do_center_crop=UpperCamelCase__     ,				**UpperCamelCase__       )
    encoding.update(UpperCamelCase__       )

    return encoding
  def 				_lowercase				(					self     :					Optional[Any]     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__     ,				**UpperCamelCase__       )





  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer.model_input_names
    __magic_name__								=			self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

